Scale-dependent measure of network centrality from diffusion dynamics

Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A node is defined to be central if it breaks th...

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Main Authors: Alexis Arnaudon, Robert L. Peach, Mauricio Barahona
Format: Article
Language:English
Published: American Physical Society 2020-07-01
Series:Physical Review Research
Online Access:http://doi.org/10.1103/PhysRevResearch.2.033104
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author Alexis Arnaudon
Robert L. Peach
Mauricio Barahona
author_facet Alexis Arnaudon
Robert L. Peach
Mauricio Barahona
author_sort Alexis Arnaudon
collection DOAJ
description Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A node is defined to be central if it breaks the metricity of the diffusion as a consequence of the effective boundaries and inhomogeneities in the graph. Our measure is naturally multiscale, as it is computed relative to graph neighborhoods within the varying time horizon of the diffusion. We find that the centrality of nodes can differ widely at different scales. In particular, our measure correlates with degree (i.e., hubs) at small scales and with closeness (i.e., bridges) at large scales, and also reveals the existence of multicentric structures in complex networks. By examining centrality across scales, our measure thus provides an evaluation of node importance relative to local and global processes on the network.
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spelling doaj.art-a63d60114e3a4fba9f1f98ec2fb1f2532024-04-12T16:57:27ZengAmerican Physical SocietyPhysical Review Research2643-15642020-07-012303310410.1103/PhysRevResearch.2.033104Scale-dependent measure of network centrality from diffusion dynamicsAlexis ArnaudonRobert L. PeachMauricio BarahonaClassic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A node is defined to be central if it breaks the metricity of the diffusion as a consequence of the effective boundaries and inhomogeneities in the graph. Our measure is naturally multiscale, as it is computed relative to graph neighborhoods within the varying time horizon of the diffusion. We find that the centrality of nodes can differ widely at different scales. In particular, our measure correlates with degree (i.e., hubs) at small scales and with closeness (i.e., bridges) at large scales, and also reveals the existence of multicentric structures in complex networks. By examining centrality across scales, our measure thus provides an evaluation of node importance relative to local and global processes on the network.http://doi.org/10.1103/PhysRevResearch.2.033104
spellingShingle Alexis Arnaudon
Robert L. Peach
Mauricio Barahona
Scale-dependent measure of network centrality from diffusion dynamics
Physical Review Research
title Scale-dependent measure of network centrality from diffusion dynamics
title_full Scale-dependent measure of network centrality from diffusion dynamics
title_fullStr Scale-dependent measure of network centrality from diffusion dynamics
title_full_unstemmed Scale-dependent measure of network centrality from diffusion dynamics
title_short Scale-dependent measure of network centrality from diffusion dynamics
title_sort scale dependent measure of network centrality from diffusion dynamics
url http://doi.org/10.1103/PhysRevResearch.2.033104
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